Recently, onboard cameras for picking up images of a situation around a vehicle are being mounted in an increasing number of vehicles such as automobiles. The images picked up by the onboard camera are extremely useful because, by detecting an object in the image, the images can be utilized to determine the possibility of a collision between the vehicle and the object or to support steering of the vehicle when parking.
Image processing technology for detecting an object within an image (image based on the picked-up image and including processing target object; hereunder, referred to as “processed image”) is being developed at a remarkable pace in recent years with a view to reducing the time required for detection while enhancing the detection accuracy. Pattern matching technology such as technology that uses, for example, HOG (histogram of oriented gradients) feature values may be mentioned as an example of such kind of technology for detecting objects.
When performing pattern matching, in some cases an object cannot be correctly detected from the processed image derived from the picked-up image, and an erroneous detection occurs. Technology for solving the problem of such erroneous detection includes, for example, technology that performs detection processing in multiple stages using a plurality of dictionaries, and utilizes only a result that was output as being detected in all of the detection processing stages as a correct detection result. According to this kind of technology, the occurrence of an erroneous detection can be reduced during pattern matching, and objects can be accurately detected from a processed image.
However, according to the above-described technology it is necessary to perform detection processing in multiple stages using a plurality of dictionaries. Consequently, in comparison to using a single dictionary, a large memory is required that has a size that corresponds to the number of dictionaries. Further, since it is necessary to perform detection processing in multiple stages, a longer time is required for the calculation operations in comparison to when using a single dictionary. Furthermore, even when using a plurality of dictionaries, in a case where the shape of an object changes in the processed images, such as when bilateral symmetry of an object is lost, a likelihood calculated by processing using the respective dictionaries becomes lower than a predetermined likelihood, and hence it becomes extremely difficult to detect the object.